Master Data Management, or MDM, is a common issue for customers across all industries — having multiple data sources makes it difficult to identify one source of truth for the data. A typical business might encounter these types of data issues:
MDM Technology assessment
When deciding whether to build vs buy a solution, the following are key factors to keep in mind when completing a technical assessment:
- Data Modeling: Managing any complex relationships and map to the master customer information requirements of the whole organization, not just selected areas
- Information Quality Management: Well-integrated facilities for cleansing, matching, linking and identifying data from different sources
- Loading, Integration & Synchronization: The solution should support the bidirectional transfer of data between the central system and peripheral systems in batch and real-time modes
- Business Services & Workflow: Data encapsulation and base for SOA applications
- Leveraging Current Technical Landscape: Evaluation of existing master data hub
- Performance, Scalability & Availability: Benchmarks available for batch and real-time integration. Horizontal/Vertical scaling available on physical and virtual environments
- Manageability & Security: Reporting on activity & data quality, integration with systems management, manage access rights and privacy
- Flexible architecture: MDM Database – CRM Vendor specific, Industry Vendor Specific or a New data hub ,Single or Multi-domain MDM solution
Additional challenges to consider as you try to make sense of all your data:
Data
- Multiple primary sources of data used by the team (Reporting Server, Marketing Data Warehouse, Marketing Analytics)
- Data redundancy, data quality, contact, account mismatch, fuzzy match issues recorded as part of the conversations
- Team generates different datasets to support daily operations (Leads, Accounts, Sales, Contracts, Customers, Owners, …)
- Team spends a lot of time debugging the datasets, because most of the business logic is embedded within the reporting layer or within the feed generation scripts (this should be part of ETL)
- Lead data consolidation issues
- Data coming from multiple vendors
- Can’t match the information to the current contracts, accounts, address etc.
- Salesforce lead suppression
Operations
- Team spends a lot of time on data consolidation and validations (BI Reports)
- Datasets take a lot of time to generate
- Lack of automation around process of feed generation and notifications
- Can’t trust the data
Platform & Infrastructure
- Customer may have best of the tools for analytics, but are not using to their full potential
- Data consolidation is required, and that can be leveraged more for BI and Analytics
- Lack of interactive reporting
Implementation models
There are a number of models for implementing a technology solution for master data management. These depend on an organization’s core business, its corporate structure and its goals. These include:
- Source of record
- Registry
- Consolidation
- Coexistence/ create golden record
- Transaction/centralized
Master Data Management (MDM) can help build a 360-degree view of key business information, bringing together data from Sales & Marketing, Regulatory Affairs, Finance, IT, Operations and Manufacturing. This allows you to take full advantage of your organization’s data for better business outcomes.
Interested in learning more about Master Data Management? Contact us today at info@springml.com
